class_docstr: Inherits time series methods from TSM class.
  
  **** DYNAMIC REGRESSION MODEL ****
  
  Parameters
  ----------
  
  formula : string
      patsy string describing the regression
  
  data : pd.DataFrame
      Field to specify the data that will be used
code: def __init__(self, formula, data):
      # Initialize TSM object
      super(DynReg, self).__init__("DynReg")
  
      # Latent variables
      self.max_lag = 0
      self._z_hide = (
          0  # Whether to cutoff variance latent variables from results
      )
      self.supported_methods = ["MLE", "PML", "Laplace", "M-H", "BBVI"]
      self.default_method = "MLE"
      self.model_name = "Dynamic Linear Regression"
      self.multivariate_model = False
  
      # Format the data
      self.is_pandas = True  # This is compulsory for this model type
      self.data_original = data
      self.formula = formula
      self.y, self.X = dmatrices(formula, data)
      self.z_no = self.X.shape[1] + 1
      self.y_name = self.y.design_info.describe()
      self.data_name = self.y_name
      self.X_names = self.X.design_info.describe().split(" + ")
      self.y = np.array([self.y]).ravel()
      self.data = self.y
      self.X = np.array([self.X])[0]
      self.index = data.index
  
      self._create_latent_variables()
  
imports: import copy
  import sys
  import numpy
  import pandas
  import scipy.stats
  from patsy import dmatrices, dmatrix, demo_data
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .kalman import *
label_desc: Use image node_id 1 to create a new DynReg object from inherited base classes: tsm with example: obj = DynReg(formula, data)